package com.tom.chapter02;


import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.AggregateOperator;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.operators.FlatMapOperator;
import org.apache.flink.api.java.operators.UnsortedGrouping;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;


/**
 * 批处理
 *
 * 先逐行读入文件数据，然后将每一行文字拆分成单
 * 词；接着按照单词分组，统计每组数据的个数，就是对应单词的频次
 *
 */
public class BatchWordCount {

    public static void main(String[] args) throws Exception {

        // 1. 创建执行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        // 2. 从文件读取数据 按行读取(存储的元素就是每行的文本)
        DataSource<String> lines = env.readTextFile("input/words.txt");

        // 3. 转换数据格式
        FlatMapOperator<String, Tuple2<String, Long>> wordAndOne = lines.flatMap((String line, Collector<Tuple2<String, Long>> out) -> {
            String[] words = line.split(" ");
            for (String word : words) {
                out.collect(Tuple2.of(word, 1L));
            }
        }).returns(Types.TUPLE(Types.STRING, Types.LONG));//当 Lambda 表达式使用 Java 泛型的时候, 由于泛型擦除的存在, 需要显示的声明类型信息;
        // 4. 按照 word 进行分组
        UnsortedGrouping<Tuple2<String, Long>> wordAndOneUG = wordAndOne.groupBy(0);

        // 5. 分组内聚合统计
        AggregateOperator<Tuple2<String, Long>> sum = wordAndOneUG.sum(1);
        // 6. 打印结果
        sum.print();

        /**
         * (flink,1)
         * (world,1)
         * (hello,3)
         * (java,1)
         */
    }
}
